Auxiliary-based Methods. Most of the recent works leverage auxiliary tasks as the prior knowledge to guide the feature learning toward more generalizable cues. Liu  [1]} proposed to employ the depth map and rPPG as strong supervision signals for live samples to regularize the features. Kim  [2]} further leveraged the reflection maps as the supervision signal for spoof samples. Many other FAS methods [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]} also heavily rely on similar auxiliary pixel-wise supervision to improve their FAS model performance. Even though the feature learning can benefit from such supervision, the pseudo ground truths for those tasks are not accurate, and the generation of those supervision signals takes high computation resources.
